Man-machine conversation method and device
A technology of human-computer dialogue and data, applied in the computer field, can solve problems such as lack of complex expressions and expression fault-tolerant processing capabilities, and achieve the effect of improving response capabilities
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Embodiment 1
[0065] Preferred Embodiment 1: After the server sends the dialogue context data to the second client, the user of the second client responds to the dialogue context data and returns the corresponding first data, and the server sends the second dialogue context data A data is returned to the first client as dialog context data.
[0066] The dialogue context data returned by this method is the data answered immediately by other users, which not only has stronger matching, but also has certain real-time performance.
Embodiment 2
[0067] Preferred Embodiment 2: After the server sends the dialogue context data to the second client, the user of the second client responds to the dialogue context data and returns the corresponding first data in several times. Then combine the received first data and return it to the first client as the dialog context data.
[0068] The dialogue context data returned by this method combines the response data input by the user multiple times. Compared with the preferred embodiment 1, the dialogue context data returned has stronger integrity and accuracy.
Embodiment 3
[0069] Preferred embodiment three: the server sends the dialogue context data to a plurality of second clients, and the users of the plurality of second clients respond to the dialogue context data and return corresponding first data respectively, and the server Then, according to the correlation between each first data and the above dialogue data, one or more first data with the highest or higher correlation (that is, the most matching or relatively matching) are given to the first client as the dialogue context data end.
[0070] The dialogue context data returned by this method is not limited to the response data returned by a single user. At the same time, the server will calculate the correlation between multiple dialogue context data and dialogue context data and return one or more dialogue contexts with high matching data, which further improves the server-side dialogue processing matching ability.
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